

import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import RegNetForImageClassification, RegNetModel
					from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor





class       snake_case_  :







			'''simple docstring'''

			def __init__(	self					:							str				,  _UpperCamelCase					:							Optional[Any]				,  _UpperCamelCase					:							Optional[Any]=3				,  _UpperCamelCase					:							int=3_2				,  _UpperCamelCase					:							Tuple=3				,  _UpperCamelCase					:							List[str]=1_0				,  _UpperCamelCase					:							Tuple=[1_0, 2_0, 3_0, 4_0]				,  _UpperCamelCase					:							Optional[Any]=[1, 1, 2, 1]				,  _UpperCamelCase					:							Any=True				,  _UpperCamelCase					:							List[Any]=True				,  _UpperCamelCase					:							Optional[Any]="relu"				,  _UpperCamelCase					:							Any=3				,  _UpperCamelCase					:							Dict=None				,  ) ->str:
									snake_case_  =      parent
									snake_case_  =      batch_size
									snake_case_  =      image_size
									snake_case_  =      num_channels
									snake_case_  =      embeddings_size
									snake_case_  =      hidden_sizes
									snake_case_  =      depths
									snake_case_  =      is_training
									snake_case_  =      use_labels
									snake_case_  =      hidden_act
									snake_case_  =      num_labels
									snake_case_  =      scope
									snake_case_  =      len(_UpperCamelCase				)







			def 							snake_case__(	self					:							Tuple				) ->Any:
									snake_case_  =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

									snake_case_  =      None
									if self.use_labels:
															snake_case_  =      ids_tensor([self.batch_size]				,  self.num_labels				)

									snake_case_  =      self.get_config()

									return config, pixel_values, labels







			def 							snake_case__(	self					:							Optional[int]				) ->Optional[Any]:
									return RegNetConfig(
									    num_channels=self.num_channels				,  embeddings_size=self.embeddings_size				,  hidden_sizes=self.hidden_sizes				,  depths=self.depths				,  hidden_act=self.hidden_act				,  num_labels=self.num_labels				,  )







			def 							snake_case__(	self					:							Any				,  _UpperCamelCase					:							Optional[int]				,  _UpperCamelCase					:							Optional[int]				,  _UpperCamelCase					:							Optional[int]				) ->Union[str, Any]:
									snake_case_  =      RegNetModel(config=_UpperCamelCase				)
									model.to(_UpperCamelCase				)
									model.eval()
									snake_case_  =      model(_UpperCamelCase				)
									# expected last hidden states: B, C, H // 32, W // 32
									self.parent.assertEqual(
									    result.last_hidden_state.shape				,  (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)				,  )







			def 							snake_case__(	self					:							Optional[int]				,  _UpperCamelCase					:							int				,  _UpperCamelCase					:							Dict				,  _UpperCamelCase					:							List[str]				) ->List[str]:
									snake_case_  =      self.num_labels
									snake_case_  =      RegNetForImageClassification(_UpperCamelCase				)
									model.to(_UpperCamelCase				)
									model.eval()
									snake_case_  =      model(_UpperCamelCase				,  labels=_UpperCamelCase				)
									self.parent.assertEqual(result.logits.shape				,  (self.batch_size, self.num_labels)				)







			def 							snake_case__(	self					:							Tuple				) ->int:
									snake_case_  =      self.prepare_config_and_inputs()
									snake_case_,       snake_case_,       snake_case_  =      config_and_inputs
									snake_case_  =      {'''pixel_values''': pixel_values}
									return config, inputs_dict






@require_torch
class       snake_case_  (						__A      ,			__A      ,			unittest.TestCase   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  Any		     =							(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
			SCREAMING_SNAKE_CASE	:  Tuple		     =							(
			    {"feature-extraction": RegNetModel, "image-classification": RegNetForImageClassification}
			    if is_torch_available()
			    else {}
			)

			SCREAMING_SNAKE_CASE	:  int		     =							False
			SCREAMING_SNAKE_CASE	:  str		     =							False
			SCREAMING_SNAKE_CASE	:  Any		     =							False
			SCREAMING_SNAKE_CASE	:  Optional[int]		     =							False







			def 							snake_case__(	self					:							str				) ->Dict:
									snake_case_  =      RegNetModelTester(self				)
									snake_case_  =      ConfigTester(self				,  config_class=_UpperCamelCase				,  has_text_modality=_UpperCamelCase				)







			def 							snake_case__(	self					:							Tuple				) ->Dict:
									self.create_and_test_config_common_properties()
									self.config_tester.create_and_test_config_to_json_string()
									self.config_tester.create_and_test_config_to_json_file()
									self.config_tester.create_and_test_config_from_and_save_pretrained()
									self.config_tester.create_and_test_config_with_num_labels()
									self.config_tester.check_config_can_be_init_without_params()
									self.config_tester.check_config_arguments_init()







			def 							snake_case__(	self					:							str				) ->Tuple:
									return







			@unittest.skip(reason='''RegNet does not use inputs_embeds'''				)
			def 							snake_case__(	self					:							Optional[Any]				) ->str:
									pass







			@unittest.skip(reason='''RegNet does not support input and output embeddings'''				)
			def 							snake_case__(	self					:							Any				) ->List[Any]:
									pass







			def 							snake_case__(	self					:							Any				) ->Optional[Any]:
									snake_case_,       snake_case_  =      self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															snake_case_  =      model_class(_UpperCamelCase				)
															snake_case_  =      inspect.signature(model.forward				)
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															snake_case_  =      [*signature.parameters.keys()]

															snake_case_  =      ['''pixel_values''']
															self.assertListEqual(arg_names[:1]				,  _UpperCamelCase				)







			def 							snake_case__(	self					:							Dict				) ->Any:
									snake_case_  =      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCamelCase				)







			def 							snake_case__(	self					:							List[str]				) ->Tuple:
									snake_case_,       snake_case_  =      self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															snake_case_  =      model_class(config=_UpperCamelCase				)
															for name, module in model.named_modules():
																					if isinstance(_UpperCamelCase				,  (nn.BatchNormad, nn.GroupNorm)				):
																											self.assertTrue(
																											    torch.all(module.weight == 1				)				,  msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''				,  )
																											self.assertTrue(
																											    torch.all(module.bias == 0				)				,  msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''				,  )







			def 							snake_case__(	self					:							int				) ->Optional[int]:
									def check_hidden_states_output(_UpperCamelCase					:							Optional[int]				,  _UpperCamelCase					:							Union[str, Any]				,  _UpperCamelCase					:							Optional[int]				):
															snake_case_  =      model_class(_UpperCamelCase				)
															model.to(_UpperCamelCase				)
															model.eval()

															with torch.no_grad():
																					snake_case_  =      model(**self._prepare_for_class(_UpperCamelCase				,  _UpperCamelCase				)				)

															snake_case_  =      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

															snake_case_  =      self.model_tester.num_stages
															self.assertEqual(len(_UpperCamelCase				)				,  expected_num_stages + 1				)

															# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
															self.assertListEqual(
															    list(hidden_states[0].shape[-2:]				)				,  [self.model_tester.image_size // 2, self.model_tester.image_size // 2]				,  )

									snake_case_,       snake_case_  =      self.model_tester.prepare_config_and_inputs_for_common()
									snake_case_  =      ['''basic''', '''bottleneck''']
									for model_class in self.all_model_classes:
															for layer_type in layers_type:
																					snake_case_  =      layer_type
																					snake_case_  =      True
																					check_hidden_states_output(_UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				)

																					# check that output_hidden_states also work using config
																					del inputs_dict["output_hidden_states"]
																					snake_case_  =      True

																					check_hidden_states_output(_UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				)







			def 							snake_case__(	self					:							str				) ->Any:
									snake_case_  =      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase				)







			@slow
			def 							snake_case__(	self					:							Dict				) ->str:
									for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															snake_case_  =      RegNetModel.from_pretrained(_UpperCamelCase				)
															self.assertIsNotNone(_UpperCamelCase				)






def     __SCREAMING_SNAKE_CASE   ():
						snake_case_  =      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
						return image





@require_torch
@require_vision
class       snake_case_  (						unittest.TestCase   ):







			'''simple docstring'''

			@cached_property
			def 							snake_case__(	self					:							Optional[int]				) ->Tuple:
									return (
									    AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)
									    if is_vision_available()
									    else None
									)







			@slow
			def 							snake_case__(	self					:							Dict				) ->str:
									snake_case_  =      RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(_UpperCamelCase				)

									snake_case_  =      self.default_image_processor
									snake_case_  =      prepare_img()
									snake_case_  =      image_processor(images=_UpperCamelCase				,  return_tensors='''pt'''				).to(_UpperCamelCase				)

									# forward pass
									with torch.no_grad():
															snake_case_  =      model(**_UpperCamelCase				)

									# verify the logits
									snake_case_  =      torch.Size((1, 1_0_0_0)				)
									self.assertEqual(outputs.logits.shape				,  _UpperCamelCase				)

									snake_case_  =      torch.tensor([-0.4180, -1.5051, -3.4836]				).to(_UpperCamelCase				)

									self.assertTrue(torch.allclose(outputs.logits[0, :3]				,  _UpperCamelCase				,  atol=1e-4				)				)

from __future__ import annotations

lowercase    :   Dict						=			tuple[int, int, int]
lowercase    :   List[str]						=			tuple[str, str, str]


# used alphabet --------------------------
# from string.ascii_uppercase
lowercase    :   int						=			'ABCDEFGHIJKLMNOPQRSTUVWXYZ'

# -------------------------- default selection --------------------------
# rotors --------------------------
lowercase    :   Optional[int]						=			'EGZWVONAHDCLFQMSIPJBYUKXTR'
lowercase    :   Optional[Any]						=			'FOBHMDKEXQNRAULPGSJVTYICZW'
lowercase    :   str						=			'ZJXESIUQLHAVRMDOYGTNFWPBKC'
# reflector --------------------------
lowercase    :   Tuple						=			{
    'A': 'N',
    'N': 'A',
    'B': 'O',
    'O': 'B',
    'C': 'P',
    'P': 'C',
    'D': 'Q',
    'Q': 'D',
    'E': 'R',
    'R': 'E',
    'F': 'S',
    'S': 'F',
    'G': 'T',
    'T': 'G',
    'H': 'U',
    'U': 'H',
    'I': 'V',
    'V': 'I',
    'J': 'W',
    'W': 'J',
    'K': 'X',
    'X': 'K',
    'L': 'Y',
    'Y': 'L',
    'M': 'Z',
    'Z': 'M',
}

# -------------------------- extra rotors --------------------------
lowercase    :   Optional[int]						=			'RMDJXFUWGISLHVTCQNKYPBEZOA'
lowercase    :   List[Any]						=			'SGLCPQWZHKXAREONTFBVIYJUDM'
lowercase    :   Dict						=			'HVSICLTYKQUBXDWAJZOMFGPREN'
lowercase    :   List[Any]						=			'RZWQHFMVDBKICJLNTUXAGYPSOE'
lowercase    :   Optional[Any]						=			'LFKIJODBEGAMQPXVUHYSTCZRWN'
lowercase    :   Tuple						=			'KOAEGVDHXPQZMLFTYWJNBRCIUS'




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     RotorPositionT		,    _lowerCamelCase					:     RotorSelectionT		,    _lowerCamelCase					:     str)   ->       tuple[RotorPositionT, RotorSelectionT, dict[str, str]]:






							'''simple docstring'''





							if (unique_rotsel := len(set(_lowerCamelCase))) < 3:
														__UpperCamelCase      :  Tuple       =   F'Please use 3 unique rotors (not {unique_rotsel})'
														raise Exception(_lowerCamelCase)

							# Checks if rotor positions are valid
							__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase      :  str       =   rotpos
							if not 0 < rotorposa <= len(_lowerCamelCase):
														__UpperCamelCase      :  Union[str, Any]       =   F'First rotor position is not within range of 1..26 ({rotorposa}'
														raise ValueError(_lowerCamelCase)
							if not 0 < rotorposa <= len(_lowerCamelCase):
														__UpperCamelCase      :  int       =   F'Second rotor position is not within range of 1..26 ({rotorposa})'
														raise ValueError(_lowerCamelCase)
							if not 0 < rotorposa <= len(_lowerCamelCase):
														__UpperCamelCase      :  List[Any]       =   F'Third rotor position is not within range of 1..26 ({rotorposa})'
														raise ValueError(_lowerCamelCase)

							# Validates string and returns dict
							__UpperCamelCase      :  str       =   _plugboard(_lowerCamelCase)

							return rotpos, rotsel, pbdict




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     str)   ->       dict[str, str]:






							'''simple docstring'''





							if not isinstance(_lowerCamelCase		,    _lowerCamelCase):
														__UpperCamelCase      :  Union[str, Any]       =   F'Plugboard setting isn\'t type string ({type(_lowerCamelCase)})'
														raise TypeError(_lowerCamelCase)
							elif len(_lowerCamelCase) % 2 != 0:
														__UpperCamelCase      :  int       =   F'Odd number of symbols ({len(_lowerCamelCase)})'
														raise Exception(_lowerCamelCase)
							elif pbstring == "":
														return {}

							pbstring.replace(" "		,    "")

							# Checks if all characters are unique
							__UpperCamelCase      :  Optional[int]       =   set()
							for i in pbstring:
														if i not in abc:
																					__UpperCamelCase      :  Tuple       =   F'\'{i}\' not in list of symbols'
																					raise Exception(_lowerCamelCase)
														elif i in tmppbl:
																					__UpperCamelCase      :  Tuple       =   F'Duplicate symbol ({i})'
																					raise Exception(_lowerCamelCase)
														else:
																					tmppbl.add(_lowerCamelCase)
							del tmppbl

							# Created the dictionary
							__UpperCamelCase      :  Union[str, Any]       =   {}
							for j in range(0		,    len(_lowerCamelCase) - 1		,    2):
														__UpperCamelCase      :  Union[str, Any]       =   pbstring[j + 1]
														__UpperCamelCase      :  str       =   pbstring[j]

							return pb




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     str		,    _lowerCamelCase					:     RotorPositionT		,    _lowerCamelCase					:     RotorSelectionT = (rotora, rotora, rotora)		,    _lowerCamelCase					:     str = ""		,    )   ->       str:






							'''simple docstring'''





							__UpperCamelCase      :  List[Any]       =   text.upper()
							__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase      :  Optional[int]       =   _validator(
							    _lowerCamelCase		,    _lowerCamelCase		,    plugb.upper())

							__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase      :  Tuple       =   rotor_position
							__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase      :  List[Any]       =   rotor_selection
							rotorposa -= 1
							rotorposa -= 1
							rotorposa -= 1

							__UpperCamelCase      :  List[str]       =   []

							# encryption/decryption process --------------------------
							for symbol in text:
														if symbol in abc:
																					# 1st plugboard --------------------------
																					if symbol in plugboard:
																												__UpperCamelCase      :  Dict       =   plugboard[symbol]

																					# rotor ra --------------------------
																					__UpperCamelCase      :  Optional[int]       =   abc.index(_lowerCamelCase) + rotorposa
																					__UpperCamelCase      :  List[Any]       =   rotora[index % len(_lowerCamelCase)]

																					# rotor rb --------------------------
																					__UpperCamelCase      :  Dict       =   abc.index(_lowerCamelCase) + rotorposa
																					__UpperCamelCase      :  Any       =   rotora[index % len(_lowerCamelCase)]

																					# rotor rc --------------------------
																					__UpperCamelCase      :  str       =   abc.index(_lowerCamelCase) + rotorposa
																					__UpperCamelCase      :  Union[str, Any]       =   rotora[index % len(_lowerCamelCase)]

																					# reflector --------------------------
																					# this is the reason you don't need another machine to decipher

																					__UpperCamelCase      :  Union[str, Any]       =   reflector[symbol]

																					# 2nd rotors
																					__UpperCamelCase      :  Optional[int]       =   abc[rotora.index(_lowerCamelCase) - rotorposa]
																					__UpperCamelCase      :  Optional[Any]       =   abc[rotora.index(_lowerCamelCase) - rotorposa]
																					__UpperCamelCase      :  str       =   abc[rotora.index(_lowerCamelCase) - rotorposa]

																					# 2nd plugboard
																					if symbol in plugboard:
																												__UpperCamelCase      :  Optional[Any]       =   plugboard[symbol]

																					# moves/resets rotor positions
																					rotorposa += 1
																					if rotorposa >= len(_lowerCamelCase):
																												__UpperCamelCase      :  Any       =   0
																												rotorposa += 1
																					if rotorposa >= len(_lowerCamelCase):
																												__UpperCamelCase      :  Tuple       =   0
																												rotorposa += 1
																					if rotorposa >= len(_lowerCamelCase):
																												__UpperCamelCase      :  List[str]       =   0

        # else:
        #    pass
        #    Error could be also raised
        #    raise ValueError(
        #       'Invalid symbol('+repr(symbol)+')')
														result.append(_lowerCamelCase)

							return "".join(_lowerCamelCase)


if __name__ == "__main__":
		lowercase    :   Optional[Any]						=			'This is my Python script that emulates the Enigma machine from WWII.'
		lowercase    :   int						=			(1, 1, 1)
		lowercase    :   Optional[Any]						=			'pictures'
		lowercase    :   Optional[Any]						=			(rotora, rotora, rotora)
		lowercase    :   Optional[Any]						=			enigma(message, rotor_pos, rotor_sel, pb)

		print('Encrypted message:', en)
		print('Decrypted message:', enigma(en, rotor_pos, rotor_sel, pb))